Storing Information for Access Using a Captured Image

ABSTRACT

An electronic device comprising circuitry configured to associate first information and at least a first portion of a first image, and circuitry configured to use a second image that includes a portion corresponding to at least the first portion of the first image to access the associated first information.

FIELD OF THE INVENTION

Embodiments of the present invention relate to storing information sothat it can be accessed using a captured image.

BACKGROUND TO THE INVENTION

It may be desirable in certain circumstances to attach information tolocations in the real world. This has previously been achieved by usingbarcodes or RFID tags attached to real world objects or by associatinginformation with absolute positions in the world.

It would be desirable to provide an alternative mechanism by whichinformation can be associated with real world locations and objects.

It would be desirable to provide a mechanism by which a user can ‘leave’information at a real world location or object so that it can be‘collected’ later by that user or another user.

BRIEF DESCRIPTION OF THE INVENTION

According to one aspect of a first embodiment there is provided anelectronic device comprising:

means for associating first information and at least a first portion ofa first image; and

means for using a second image that includes a portion corresponding toat least the first portion of the first image to access the associatedfirst information.

It should be noted that a single electronic device comprises both meansi.e. it is capable of both associating information with an image andusing an image to access information. The information may be storedcentrally, in which case a plurality of such electronic devices are ableto both place content using an image and retrieve content using animage, that is both placement and access to information is distributed.

The first information may be media such as an image, a video or an audiofile or it may be, for example, an instruction for performing a computerfunction.

Correspondence between the portion of the second image and the firstportion of the first image does not necessarily result in automaticaccess to the associated first information. The access may beconditional on other factors.

The first information may be pre-stored for access or dynamicallygenerated on access.

According to another aspect of the first embodiment there is provided amethod of storing information for future access by others comprising:associating first information and at least a first portion of a firstimage in a database controlled by a third party so that the firstinformation can be accessed by others using a second image that includesa portion corresponding to at least the first portion of the firstimage.

According to another aspect of the first embodiment there is provided asystem for storing information comprising: a server having a databasethat has a plurality of entries each of which associates one of aplurality of image portions with respective information; a first clientdevice comprising a camera for capturing, at a first time, a first imagethat includes a first portion and means for enabling association, at thedatabase, of the first portion with first information; and a secondclient device comprising: a camera for capturing, at a second latertime, a second image, which includes a portion corresponding to at leastthe first portion of the first image; means for using the second imageto access, at the database, the associated first information; and outputmeans for outputting the accessed first information.

The first portion may be the whole or a part of an area associated withthe first image.

In implementations of this embodiment of the invention, features in acaptured ‘model’ image are used to index information. Then if a latercaptured ‘scene’ image corresponds to a previously captured model imagebecause some of the features in the captured ‘scene’ image arerecognised as equivalent to some of the features of the model image, theinformation indexed by the corresponding model image is retrieved.

According to one aspect of a second embodiment there is provided amethod for producing an homography that maps plural interest points of afirst image with interest points in a second image, comprising:

a) generating a set of putative correspondences between interest pointsof the first image and interest points of the second image;

b) making a weighted sample of correspondences from the generated set;

c) computing an homography for the sampled correspondences;

d) determining the support for that homography from the generated set;

e) repeating steps c) to d) multiple times; and

f) selecting the homography with the most support.

According to another aspect of the second embodiment there is provided amethod for producing an homography that maps a plural interest points ofa first image with interest points of at least one of a plurality ofsecond images, comprising:

a) generating a set of putative correspondences between interest pointsof the first image and interest points of a second image;

b) making a weighted sample of correspondences from the generated setwhere the probability of sampling a particular putative correspondencedepends upon a measure of probability for the interest point of thesecond image defining that particular putative correspondence;

c) computing an homography for the sampled correspondences;

d) determining the support for that homography from the generated set;

e) repeating steps c) to d) multiple times;

f) changing the second image and returning to step a), multiple times;

g) selecting the second image associated with the homography with themost support;

h) updating the measure of probability for each of the interest pointsof the selected second image that support the homography associated withthe selected second image.

According to one aspect of a third embodiment there is provided a methodfor producing an homography that maps a significant number of interestpoints of a first image with interest points in a second image,comprising:

a) generating a set of putative correspondences between interest pointsof the first image and interest points of the second image;

b) making a sample of correspondences from the generated set;

c) computing an homography for the sampled correspondences;

d) determining the support for that homography from the generated set;

e) repeating steps c) to d) multiple times;

f) selecting the homography with the most support; and

g) verifying the homography by verifying that the first and secondimages match.

According to one aspect of a fourth embodiment there is provided amethod for producing an homography that maps plural interest points of afirst image with interest points in a second image, comprising:

a) generating a set of putative correspondences between interest pointsof the first image and interest points of the second image;

b) making a sample of correspondences from the generated set;

c) computing an homography for the sampled correspondences;

d) determining the support for that homography from the generated set bydetermining the cost of each putative correspondence, wherein the costof a putative correspondence is dependent upon statistical parametersfor the interest point of the second image defining that putativecorrespondence;

e) repeating steps c) to d) multiple times; and

f) selecting the homography with the most support.

g) updating the statistical parameters for the interest points of thesecond image in dependence upon the cost of the putative correspondencesunder the selected homography.

According to another aspect of the fourth embodiment there is provided amethod for producing an homography that maps a plural interest points ofa first image with interest points of at least one of a plurality ofsecond images, comprising:

a) generating a set of putative correspondences between interest pointsof the first image and interest points of a second image;

b) making a sample of correspondences from the generated set;

c) computing an homography for the sampled correspondences;

d) determining the support for that homography from the generated set bydetermining the support from each putative correspondence, wherein thesupport from a putative correspondence is dependent upon statisticalparameters for the interest point of the second image defining thatputative correspondence;

e) repeating steps c) to d) multiple times;

f) changing the second image and returning to step a), multiple times;

g) selecting the second image associated with the homography with themost support;

h) updating the statistical parameters for the interest points of theselected second image

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the present invention reference will nowbe made by way of example only to the accompanying drawings in which:

FIG. 1 illustrates a system 10 by which one of a plurality of differentusers can bind information to any location by taking an image of thatlocation;

FIG. 2 presents the process 20 for creating a new model user image keyfrom an image captured by a user and for associating information withthis key;

FIG. 3 presents the process for retrieving information from the database8 using an image captured by a user;

FIG. 4 illustrates a process for finding an homography, H_(ms), thataligns a significant number of the interest points of the scene userimage key with interest points in one of the model user image keysstored in the database;

FIG. 5 presents the process 50 for adding new information to a modeluser image key already in the database 8 given an appropriate imagecaptured by a user; and

FIG. 6 illustrates the process of augmenting the image captured by theuser with information.

DETAILED DESCRIPTION OF EMBODIMENT(S) OF THE INVENTION

FIG. 1 illustrates a system 10 by which one of a plurality of differentusers can bind information (digital content) to any location in theworld by taking an image of that location. The digital content thennotionally exists at that location and can be collected by the same useror a different user by taking an image of that location.

A user 3A uses a mobile imaging device 2A to capture an image of alocation. The mobile imaging device 2A is in this example networkenabled and it can operate as a client to a server 6. It communicateswith the server 6 via a network 4. The imaging device 2A may, forexample, be a mobile cellular telephone that operates in a mobilecellular telecommunications network 4.

In this example, the mobile imaging device comprises a processor 11 thatwrites to and reads from memory 12 and receives data from and sends datato radio transceiver 13 which communicates with the network 4. Theprocessor 11 receives input commands/data from an audio input device 17such as a microphone, a user input device 16 such as a keypad orjoystick and a digital camera 15. The processor 11 providescommands/data to a display 14 and an audio output device 18 such as aloudspeaker. The operation of the imaging device 2A is controlled bycomputer program instructions which are loaded into the processor 11from the memory 12. The computer program instructions may be providedvia a computer readable medium or carrier such as a CD-ROM or floppydisk or may be provided via the cellular telecommunications network.

The captured image is then uploaded from the client 2A to the server 6via the network 4 in an Upload Message, which may be an MMS message. Theoriginating user 3A uses the client device 2A to communicate with theserver 6 via the network 4 and a target region is defined in the image.The target region is then processed at the server 6 to create a modeluser image key for that location. The originating user 3A definesdigital content that is to be associated with the target region of thecaptured image. If this digital content is stored at the client device2A it is uploaded to the server 6. The server 6 comprises a database 8that links model user image keys with their associated digital content.

The same user 3A or a different user 3B can subsequently obtain thedigital content associated with a location (if any) by capturing animage of the location, using their respective imaging device 2A, 2B, andby sending the image to the server 6 in a Request Message which may bean MMS message. The server 6 responds to this message by creating ascene user image key for the image received in the Request Message. Itthen searches its database 8 to see if the scene user image keycorresponds to a model user image key stored in the database 8. If thereis correspondence, the digital data linked by the database 8 to thecorresponding model user image key is obtained.

For non augmented reality digital content, the scene user image keysimply acts as a trigger for downloading the obtained digital content tothe requesting client device 2A, 2B. For augmented reality content, thecaptured image received in the Request Message is used as a coordinatesystem to place the obtained digital content within the image and theaugmented image is returned to the requesting client device. Foraugmented reality content, the user defines an area where the digitalcontent is to appear when the digital content is defined. This area maycorrespond to the target region.

If certain digital content is notionally associated with a location,then any user 3A, 3B may be able to augment the digital contentassociated with that location with additional digital content. An imageof the location is captured and the captured image is uploaded from theclient 2A, 2B to the server 6 via the network 4 in an Update Message,which may be an MMS message. The server 6 responds to this message bycreating a scene user image key for the image received in the UpdateMessage. It then searches its database 8 to see if the scene user imagekey corresponds to a model user image key stored in the database 8. Ifthere is correspondence, the digital data linked by the database 8 tothe corresponding model user image key is obtained and augmented withthe additional digital content.

It should be appreciated that although in the preceding description userimage key creation occurred at the server 6, it is also possible to havethe client device 2A, 2B perform this process.

It should be appreciated that although a system 10 has been described,the invention may also be used wholly within a single device. Forexample, a single device may operate as both client and server, with thedatabase 6 being stored in the device. The Upload message, RequestMessage and Update Message would then be messages transmitted within thedevice as opposed to externally transmitted MMS messages.

It should be appreciated that although a single device may operate as aimaging device and a client device, in other implementations they may beseparate devices.

The implementation of the invention is described in more detail in FIGS.2 to 6.

FIG. 2 presents a process 20 for creating a new model user image keyfrom a ‘model’ image captured by a user and for associating digitalcontent with this key.

To place digital content at a new location in the world a ‘model’ imageof that location is captured by a user 3A using the imaging device 2A atstep 21.

The user will usually intend for digital content to be associated withan object present in the captured image or a part of the captured imagerather than the complete image. For example the user might wish toassociate content with a sign or poster present in the image. The user,at step 22, defines the target region to be associated with digitalcontent.

If augmented content is to be used at this target and the aspect ratioof the content is to be preserved in the rendering then the aspect ratioof the target region, that is the ratio of its width to its height, mustbe known. This can either be supplied by the user or estimated from theshape of the target region.

If the imaging device 2A is a networked mobile device then this devicemay be used to define the target region. If the imaging device is adigital camera, then the captured image is loaded into software runningon a desktop computer or similar to allow definition of the targetregion.

The user 3A may manually define the target region of interest in thecaptured image by positioning four corner points on the image to definea quadrilateral. The points may, for example, be positioned via a simplegraphical user interface that allows the user to drag the corners of aquadrilateral. In one implementation, on a mobile telephone, four keys2, 8, 4, 6 of a keypad, such as an ITU standard keypad, are used to movethe currently selected point respectively up, down, left or right.Another key, for example the 5 key, selects the next corner with thefirst corner being selected again after the last. A further key, forexample, the 0 key indicates that the target region is complete. Analternative method for positioning the points is to have the user movethe mobile telephone so that displayed cross-hairs point at a cornerpoint of the quadrilateral and press a key to select. The mobiletelephone determines which position in the previously captured imagecorresponds to the selected corner region.

A semi-automatic process can be employed in which an algorithm is usedto find quadrilateral structures in the image and propose one or more ofthese as potential target regions. The user can then simply accept aregion or else elect to define the region entirely manually.

If the shape of the target region quadrilateral is defined manually bythe user it may be constrained to be one that is in agreement with theimage perspective to aid the manual selection process. The capturedimage is processed to determine the “horizon” where parallel structuresin the captured image intersect. The parallel sides of the quadrilateraltarget region are positioned in the image so that they also intersect atthe horizon.

A model user image key for indexing the content database is thenautomatically created at step 23 using the image just captured by theuser. Only parts of the image contained within the target region definedin the previous stage are used in key creation.

An image key contains: the captured image and interest points extractedby processing the image. It, in this example, also contains statisticalparameters associated with the image interest points and, optionally, adescription of the location of the image in the world.

Various methods can be used to determine interest points. For example,Hartley and Zisserman (“Multiple View Geometry in Computer Vision”,Richard Hartley and Andrew Zisserman, Cambridge University Press, secondedition, 2003) s4.8 use interest points defined by regions of minima inthe image auto-correlation function. Interest points may also be definedusing Scale invariant Feature Transform (SIFT) features as described in“Distinctive Image Features from Scale-Invariant Keypoints”, David G.Lowe, International Journal of Computer Vision, 60, 2 (2004), pp.91-110.

The statistical parameters are adaptive. They are initially assigned adefault value but become updated when the model user image keysuccessfully matches new scene user image keys in the future.

If the location of the user 3A is known when capturing the image thenthis is stored as part of the model user image key at step 24. Thelocation may, for example, be derived in a mobile cellular telephonefrom the Cell ID of the current cell, from triangulation usingneighbouring base stations, using Global Positioning System (GPS) or byuser input.

At step 25, the user 3A defines the digital content that is to beassociated with the captured image. How the user 3A specifies thedigital content is application specific. When specifying content forstorage the user 3A may select content that exists on their mobiledevice 2A. This digital content may have been created by the user or bya third party. The digital content may be a static image (and optionallyan alpha mask needed for image blending), a static 3d model, video,animated 3d models, a resource locator such as a URL, sound, text, dataetc.

If the digital content is to be used in augmented reality, then it isadditionally necessary for a user to specify where in the imagedlocation the digital content should appear. The user may separatelydefine an area using a quadrilateral frame on the captured image forthis purpose. However, in the described implementation the target regionis used to define the area.

At step 26, the digital content is stored in the database 8, indexed bythe created model user image key.

FIG. 3 presents the process for retrieving digital content from thedatabase 8 using a scene image captured by a user.

To retrieve digital content associated with a particular location in theworld an image of that location is captured by a user 3A, 3B in step 31using an imaging device 2A, 2B. In general this will be done on anetworked mobile device but this could also be done on a sufficientlypowerful network-less device if the database 8 is stored on and theprocessing run on the device.

At step 32 a scene user image key is created using the captured image.The process is the same as described for step 23 in FIG. 2 except thatthe whole image rather than a part (the target region) of the capturedimage is processed to determine the interest points. The locationinformation includes the current location of the imaging device when theimage was captured, if known. The created scene user image key is sentto the database 8 in a Request message.

Although statistical parameters may be included in a scene user imagekey they are not generally adaptive in this implementation as they arefor a model image key.

The request message may also contain an application identifier. Aparticular application might only be concerned with a small subset ofthe model user image keys in the database in which case only therelevant keys need to be considered. The application identifier enablesthis subset to be identified as illustrated in step 33. For example, atreasure hunt application might only require the user to visit a smallnumber of particular locations even though the database contains manymore keys for other applications. By considering only the relevant keysboth the computation load of matching keys and the potential for erroris reduced.

The number of model user image keys in the database 8 that are to becompared to the received image key may be reduced by considering onlythose stored image keys that have a location the same as or similar tothe user image key in the query. This process is illustrated in step 34.The use of location information may be application dependent. Forexample, in a game where a user collects images of generic road signsthe application is not concerned about the location of the sign but onlyits appearance.

Although in FIG. 3 step 34 follows step 33, in other implementationsstep 34 may precede step 33.

The sample of model user keys from the database that are to be used forcomparison with the current scene user key may consequently beconstrained by the application used and/or by the location associatedwith the scene user image key or may be unconstrained. The fouralternative are illustrated in the Figure.

At step 35 it is attempted to find a match between the scene user imagekey created at step 32 and a model user image key from the sample ofmodel user image keys from the database 8. Matching the scene user imagekey to a model user image key stored in the database involves finding anhomography, H_(ms), that aligns a significant number of the interestpoints of the scene user image key with interest points in one of themodel user image keys stored in the database. It is possible but notnecessary for the scene image to contain all of the target region of themodel image. The scene image need only contain a reasonable proportionof the model image. A suitable process 40 is illustrated in more detailin FIG. 4. It uses the Random Sample Consensus (RANSAC) algorithm whichis described in Hartley and Zisserman s 4.8 and algorithm 4.6 thecontents of which are incorporated by reference. The homography producedby RANSAC maps pixels from one image to another image of the same planarsurface and enables the recognition of objects from very differentviewpoints.

Referring to FIG. 4, at step 41 a set of putative correspondencesbetween the interest points of the scene user image key (scene interestpoints) and the interest points in a first one of the model user imagekeys stored in the database (model interest points) is determined.Typically, each scene interest point may match to multiple modelinterest points and vice versa. It is useful to filter the putativematches so that at most one match exists for each interest point. Thiscan be done by ordering the putative matches into a list with the bestmatches occurring first. The list is then descended and a record is madeof which scene and model interest points have been encountered. If aputative match is found in the list for which the scene or modelinterest point has already been encountered then the match is removedfrom the list.

The RANSAC algorithm is applied to the putative correspondence set toestimate the homography and the correspondences which are consistentwith that estimate.

The process is iterative, where the number of iterations N is adaptive.A loop is entered at step 42A. The loop returns to step 42A, where aloop exit criterion is tested and the criterion is adapted at step 42Bwhich is positioned at the end of the loop before it returns to step42A.

In each loop iteration, a random sample of four correspondences isselected at step 43A and the homography H computed at step 43B. Then, acost (distance) is calculated for each putative correspondence under thecomputed homography. The cost calculates the distance between aninterest point and its putative corresponding interest point aftermapping via the computed homography. The support for the computedhomography is measured at step 43C by the number of interest points(inliers) for which the cost is less than some threshold. After the loopis exited, the homography with most support above a threshold level ischosen at step 44. Further step 45 may be used to improve the estimateof the homography given all of the inliers. If the support does notexceed the threshold level then the process moves to step 48.

An additional verification phase may occur after step 45 at step 46 toensure that the image (scene image) associated with the scene user imagekey matches the image (model image) associated with the found model userimage key, rather than just the interest points matching. Verificationis performed by matching pixels in the target region of the model imagewith their corresponding pixels in the scene image. The correspondencebetween model and scene pixels is defined by the model to scenehomohraphy H_(ms) defined earlier. Our preferred implementation is basedon the normalised cross correlation measure of the image intensitiesbecause this is robust to changes in lighting and colour. The normalisedcross correlation measure (NCC) is calculated as follows:

${NCC} = \frac{\Sigma \; {I_{m}^{2}\left( {x,y} \right)}}{\sqrt{\Sigma \; {I_{m}^{2}\left( {x,y} \right)}\Sigma \; {I_{s}^{2}\left( {H_{ms}\left\lbrack {x,y} \right\rbrack} \right)}}}$

Where I_(m)(x,y) is the intensity of a model image pixel at location(x,y) and I_(s)(x,y) is the intensity of a scene image pixel at location(x,y). The intensity of an image pixel is simply the average of thepixels colour values, usually I(x,y)=[R(x,y)+(G(x,y)+B(x,y)]/3. Thesummation is done over all pixel locations in the model image that are(1) contained within the model target region and (2) lie within thebounds of the scene image when mapped using the homography H_(ms).Condition (2) is necessary since the scene image may only contain a viewof part of the model target region. Verification is successful if theNCC measure is above a specified threshold. In our implementation weused a threshold of 0.92. If verification is successful, then H_(ms) isreturned at step 47. If verification is unsuccessful the process movesto step 48.

At step 48, the model image is updated to the next model image and theis process returns to step 41. At step 41 a set of putativecorrespondences between the interest points of the scene user image key(scene interest points) and the interest points in the new model userimage key (model interest points) is determined and then the loop 41A isre-entered. If there are no remaining untested model user image keys inthe database at step 48, then the process moves to step 49 where afailure is reported.

Thus the RANSAC process is repeated for each possible model user imagekey in the database until the support for a chosen homography exceeds athreshold and the scene image and corresponding model image areverified. Such a match indicates a match between the model user imagekey associated with the chosen homography and the scene user image key.

In the preceding description, it has been assumed that the loop 41A, isexited only when N iterations have been completed. It otherimplementations, early so termination of the loop 41A is possible if thenumber of inliers counted at step 32C exceeds a threshold. In thisimplementation, if the verification fails at step 46 then the processmoves to step 42B in loop 41A if the loop 41A was terminated early butmoves to step 48 if the loop 41A was not terminated early.

Returning to FIG. 3, after a match has been found between a scene userimage key and a model user image key, the statistical parameters of themodel image key are updated at step 36 (step 47 in FIG. 4). Then at step37 the digital content associated with the matched model user image keyis obtained from the database 8.

In the update at step 36 the following model image key statistics aredetermined from the previous M successful matches of the model. Thesestatistics are used to improve the performance of the RANSAC matchingalgorithm.

-   1. For each model interest point, the mean and variance of the    distance (cost) between the model interest point and the    corresponding matching scene image point when mapped back into the    model image.-   2. The frequency of a model interest point being an inlier in a    matched scene image

When a model has successfully matched to a scene there is acorrespondence between model interest points and scene interest pointsand an estimated homography, H_(ms), that maps model coordinates toscene coordinates. Similarly, the inverse of H_(ms), namely H_(sm), mapsscene coordinates to model coordinates. In an ideal situation thismapping will map scene interest points to the exact position of theircorresponding model interest point. In practice there will be somevariation in this position. For each model interest point we measure themean and variance of the positions of corresponding scene image pointswhen mapped back into the model image.

This statistic is used in the RANSAC algorithm to determine whether aputative match between a model interest point and a scene interest pointis an inlier given an homography. As described in the RANSAC algorithmearlier the classification of a putative match as an inlier is done ifthe distance (cost) between the model and scene positions is below aspecified distance threshold. Rather than setting a fixed distancethreshold we use the measured mean and variance. A putative match isclassified as an inlier if the scene interest point, when mapped by thehomography into the model image, is within 3 standard deviations of themean.

The RANSAC algorithm may be improved by recording and using thefrequency of matching correspondence for each interest point of a modelimage. The frequency of matching correspondence is the frequency withwhich each interest point of the model user image key has acorrespondence with an interest point of a matching scene user image keyie. the frequency at which each model interest point is classified as aninlier when the model has been successfully matched. The frequency ofmatching correspondence is calculated in FIG. 4 at step 47 (step 36 inFIG. 3). This frequency of matching correspondence is then stored in thestatistical parameters of the matching model user image key. The sampleof the four correspondences made at step 43A may be a weighted randomselection. The probability of selecting an interest point of the modelis weighted according to its frequency of matching correspondence. Thehigher the frequency of matching correspondence the greater theweighting and the greater the probability of its selection. In ourimplementation weighted sampling reduces the number of iterationsnecessary to find a good homography by a factor of 50 on average. Thisalso filters out erroneous and unreliable model interest points from thematching process and also from future matching processes involvingdifferent scene images. When using a weighted random selection ofinterest points the weights should be considered when calculating thenumber of iterations N at step 42B. In the referenced text, Hartley &Zisserman Algorithm 4.5, the probability that an inlier point isselected, w, assumes uniformed random selection and is defined as theratio of the number of inliers to the total number of points. To accountfor the weighted sampling this is trivially reformulated as:

$w = \frac{\sum\limits_{i \in {{inlier}\mspace{14mu} {points}}}W_{i}}{\sum\limits_{i \in {{all}\mspace{14mu} {points}}}{Wi}}$

Where W_(i) is the weight associated with the I^(th) interest point.When all W_(i)'s are constant this is equivalent to the originalformulation in the referenced text.

FIG. 5 presents the process 50 for adding new digital content to a modeluser image key already in the database 8 given an appropriate imagecaptured by a user. This process is largely the same as that describedin FIG. 3 (differences at steps 51, 52) and similar references numbersdenote similar steps. However, step 37 is replaced by steps 51 and 52.At step 51, the additional digital content for storage in associationwith the matched model user image key is defined and at step 52 thisadditional digital content is stored in the database where it is indexedby the matched mode user image key.

The process of augmenting the image captured by the user with the imagedigital content obtained from the database in step 37 of FIG. 3 isillustrated in FIG. 6. Rendering the digital image augmented with thedigital content comprises two distinct phases. First the content toscene mapping is determined. This maps pixels (in the case of imagebased content), 2d vertices (in the case of 2d vector drawings) or 3dvertices (in the case of 3d models) from model coordinates to scenecoordinates. Next this mapping is used to render the image content intothe scene.

At step 61, a digital content to canonical frame mapping T_(c0) iscalculated. It is convenient to define an intermediate canonical framewhen determining the mapping of content to the scene The canonical frameis a rectangular frame with unit height and a width equal to the aspectratio of the rectangular piece of the world defined by the targetregion. Aspect ratio is defined as the ratio of width to height, i.e.,width/height.

The purpose of this mapping it to appropriately scale and position thedigital content so that it appears correctly when finally rendered intothe scene image. For the purpose of our implementation we transform thecontent so that:

-   -   1. It is at the largest possible scale that fits into the target        region detected in the scene image    -   2. The contents aspect ratio is preserved    -   3. The content is centred either horizontally or vertically to        balance out any remaining space.

If a point in the digital content frame is given by p_(c) then theequivalent point p_(c) in the canonical frame is given by theexpression:

p ₀ =T _(c0) p _(c)

For 2d content T_(c0) is a 3×3 matrix and content and canonical pointsare defined in homogeneous coordinates as 3 element column vectors:

p ₀=[x ₀ y ₀1]^(T)

p _(c)=[x _(c) y _(c) w _(c)]^(T)

The mapping T_(c0) is given by the expression:

$T_{c\; 0} = \begin{bmatrix}s & 0 & \frac{w_{0} - {sw}_{c}}{2} \\0 & s & \frac{1 - {sh}_{c}}{2} \\0 & 0 & 1\end{bmatrix}$

Where s is the scale factor given by the expression:

If (w _(c) /h _(c) >w ₀) then s=w ₀ /w _(c) otherwise s=1/h _(c)

Where w_(c) is the width of the content, h_(c) is the height of thecontent and w₀ is the width of the canonical frame (which is also theaspect ratio of the target location).

For 3d content T_(c0) is calculated in an analogous way but it is now a4×4 matrix and the content vertices are 3d points represented inhomogeneous coordinates by 4 element column vectors.

At step 62, the canonical frame to Model Mapping H_(0m) is calculated.This mapping takes the four corners of the rectangular canonical frameand maps them to the four vertices of the target region quadrilateral ofthe model image. Since all points lie on planes this mapping can bedescribed by a 3×3 homography matrix and can be determined using thedirect linear transformation (DLT). Note again that the 2d vertexcoordinates are described in homogeneous coordinates using 3 elementcolumn vectors. The DLT algorithm for calculating an homography givenfour points is described by Hartley and Zisserman in s. 4.1. andalgorithm 4.1, the content of which are herby incorporated by reference.

At step 63, the canonical frame to scene Mapping T_(0s) is calculated.For 2d content the mapping from the canonical frame to the scene issimply determined by concatenating the mapping from the canonical frameto the model and the mapping from the model to the scene. The mappingfrom the model to the scene is the output of the image key matchingprocess 40 and is given by the homography H_(ms). The mapping from thecanonical frame to the scene is still an homography and is given by theexpression:

T _(0s) =H _(ms) H _(0m)

For 3d content T0s is a projection from 3d to 2d represented by a 3×4element matrix. This can be determined using standard techniques forcamera calibration such as the DLT. Camera calibration requires a set ofcorresponding 3d vertices and 2d points for which we use the 2d sceneand model interest points and the 2d model interest points mapped intothe canonical frame and given the extra coordinate z=0.

At step 64, the content to scene mapping T_(cs) is calculated bycombining the mappings calculated in steps 63 and 61.

T _(cs) =T _(0s) T _(c0)

At step 65, the digital content is rendered into the Scene using T_(cs)For 2d content the content to scene mapping is used directly to draw thecontent into the scene. There are many algorithms in the literature todo this for image and vector type graphics. One example, for renderingimage content is to iterate over every pixel in the scene target regionand calculate the corresponding pixel in the content frame using theinverse of the content to scene transformation. To avoid aliasing weperform bilinear sampling of the content to determine the value of thepixel to render into the scene. Our system also supports the use of analpha mask which can be used to blend the scene and content pixels tocreate effects such as transparency and shadows. The alpha mask issimply a greyscale image with the same dimensions of the content and itis used in the standard way to blend images.

The rendering of 3d content is performing using standard 3d renderingsoftware such as OpenGL or DirectX. The mapping T_(0s) defined above isanalogous to the camera matrix in these rendering systems.

Another application of the invention is in ‘texture mapping’. In thiscase, digital content is associated with an image portion that mayappear in many captured images. The image portion, when it appears in acaptured image, triggers the augmentation of the captured image usingthe digital content.

Although embodiments of the present invention have been described in thepreceding paragraphs with reference to various examples, it should beappreciated that modifications to the examples given can be made withoutdeparting from the scope of the invention as claimed.

Whilst endeavoring in the foregoing specification to draw attention tothose features of the invention believed to be of particular importanceit should be understood that the Applicant claims protection in respectof any patentable feature or combination of features hereinbeforereferred to and/or shown in the drawings whether or not particularemphasis has been placed thereon.

I/We claim: 1-35. (canceled)
 36. An electronic device comprising:circuitry configured to associate first information and at least a firstportion of a first image; and circuitry configured to use a secondimage, that includes a portion corresponding to at least the firstportion of the first image, to access the associated first information.37. An electronic device as claimed in claim 36, further comprising: acamera for capturing the first image that includes the portion forassociation with the first information and for capturing, at a latertime, the second image, which includes a portion corresponding to atleast the portion of the first image, for accessing the associated firstinformation; and an output configured to output the accessedinformation.
 38. An electronic device as claimed in claim 36 furthercomprising a user input configured to enable selection of a targetregion on the image.
 39. An electronic device as claimed in claim 38,wherein a shape of the target region is configured to be automaticallyconstrained to conform to a perspective of the first image.
 40. Anelectronic device as claimed in claim 38, further comprising at leastone processor configured to determine interest points within the targetregion.
 41. An electronic device as claimed in claim 40, wherein theinterest points are configured to be used to define a model image keyfor association with the first content in a database.
 42. An electronicdevice as claimed in claim 41, further comprising circuitry configuredto include a location at which the first image was captured in the modelimage key.
 43. An electronic device as claimed in claim 36 comprising auser input configured to enable selection of an area, at least a portionof which is the first portion, in the first image with which the firstinformation is associated.
 44. An electronic device as claimed in claim36 comprising circuitry configured to compose the first information. 45.An electronic device as claimed in claim 36, comprising a transmitterfor sending a message to a server having a database that associates eachof a plurality of image portions with respective information, whereinthe message is dependent upon the first portion of the first image andenables the association of the first portion of the first image with thefirst digital data in the database.
 46. An electronic device as claimedin claim 36, comprising a transmitter for sending a message to a serverhaving a database that associates each of a plurality of image portionswith respective information, wherein the message is dependent upon thesecond image.
 47. An electronic device as claimed in claim 46, whereinthe message identifies the location at which the second image wascaptured.
 48. An electronic device as claimed in claim 46, wherein themessage identifies an application running on the electronic device. 49.An electronic device as claimed in claim 36, comprising at least oneprocessor configured to determine interest points within the first imageand using the interest points to define a scene key, wherein theinformation is for augmenting existing information already associatedwith the portion of the first image.
 50. A method of storing informationfor future access by others comprising: associating first informationand at least a first portion of a first image in a database controlledby a third party so that the first information can be accessed by othersusing a second image that includes a portion corresponding to at leastthe first portion of the first image.
 51. A system for storinginformation comprising: a server having a database that has a pluralityof entries each of which associates one of a plurality of image portionswith respective information; a first client device comprising a camerafor capturing, at a first time, a first image that includes a firstportion and where the first client device is configured to associate, atthe database, the first portion with first information; and a secondclient device comprising: a camera for capturing, at a second latertime, a second image, which includes a portion corresponding to at leastthe first portion of the first image; where the second client device isconfigured to use the second image to access, at the database, theassociated first information; and an output configured to output theaccessed first information.
 52. A system as claimed in claim 51, whereinthe electronic device comprises a transmitter for sending a message tothe server that is dependent upon the second image and identifies thelocation at which the second image was captured, wherein the locationreceived in the message is usable by the database to simplify the taskof finding a correspondence between the second image and a databaseentry.
 53. A system as claimed in claim 51, wherein the electronicdevice comprises a transmitter for sending a message to the server thatis dependent upon the second image and identifies an application runningon the second client device, wherein the identity of the applicationreceived in the message is usable by the database to simplify the taskof finding a correspondence between the second image and a databaseentry.
 54. A computer program product comprising a non-transitorystorage medium comprising computer program instructions, which whenloaded into a processor, enable the method of claim
 50. 55. Anon-transitory computer readable medium embodying a computer programcomprising computer program instructions, which when loaded into aprocessor, enable the method of claim 50.